Data Decision Analytics.
Data Decision Analytics.
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Industry Challenge
Industry Challenge
Most facility management organizations are data rich and information poor. Our teams do not know what to do with the data flowing through internal systems. To add to the complex nature of the business, facility managers struggle keeping up with a reactive workload while extending life of assets, making smart replacement decisions while minimizing operational disruptions to their properties. While we can certainly ‘manage’ through status-quo of the never-ending fire drill, it is imperative to evolve from reactive asset management if we wish to lower total cost of ownership and deliver more with existing resources and technology. Organizations need to leverage readily available data to derive agile insights and make intelligent decisions.
The Ask
The Ask
Using information and data currently available, develop a modeling tool that helps identify, prioritize, and schedule repair and replacement of different asset types across 2000 location portfolio to help alleviate year-over-year expense budget increases
Initial Implementation
Initial Implementation
Identify most reactive accounts across portfolio, by volume and expense. Where is the organization's FM teams spending most time and resources: Roofing, HVAC, Fire suppression systems. These assets are directly tied to both comfort AND life safety of customers and employees.
Determine what data we have and how we are using it to make decisions. Historical WO record review across these 3 specific categories revealed mostly standardized specification across the fleet. Additional data points such as asset age, property grand opening day, WO counts, WO CapEx, were captured, consolidated and added to working database.
Initial regression analysis and identification of priority assets. Using the above mentioned data points, a mathematical model was developed to provide a health score (1-10) relative to others in the category. This score is then used to identify which specific assets where immediate replacement was needed to stop budget hemorrhage (8 - 10) and which ones were trending in-line (5 - 7) or below expense averages (1 - 4).
Making a Difference
Making a Difference
While the initial analysis identified 20% of locations driving 80% of spend for replacement, it wasn’t enough to help evolve the maintenance model from reactive to proactive. A new and innovative approach was needed.
Additional forces driving accelerated asset degradation. Weather patterns, salinity of air cycles, engine emissions, water quality, etc. all these factors can accelerate asset degradation over time, moving anticipated replacement timeline ahead of schedule.
Deeper insights, evolving the industry. Folding these ‘additional forces’ into the model allowed teams to predict future replacement timing, moving away from previous age-based models to more proactive, cost-effective, condition-based replacement strategies.
Making it easy. Interactive Tableau dashboard developed to allow automatic data refreshes and agile portfolio management, site/need identification, multi-year budget forecasting and overall positive brand impact.
Quantified Results
Quantified Results
From reactive to proactive: 80% reduction in reactive WO generation by proactively tackling 'noisy', costly to maintain assets.
Lower total cost of ownership: 95% accuracy at identifying locations AND timing for asset replacement, WITHOUT surveying, eliminating $250,000 yearly survey costs across these 3 categories
Compressing annual budget planning cycle: from 5 months to 3 weeks
Incremental bandwidth: Freed up FM team's capacity to work on more productive and valuable work
Additional Thoughts
Additional Thoughts
Beyond initial 3 asset categories, enough data exists TODAY within any organization's CMMS (Computerized Maintenance Management Systems) to continue to push industry from reactive to proactive and predictive states. Other accounts that can and should be evaluated: Parking Lots, Exterior Paint and Exterior Illuminated Signage.
FM organizations should consider asset complexity, non-standardized specifications and long term value when evaluating potential benefit of changing maintenance models, as efficacy of forecast depends largely on these factors i.e., predicting replacement for door locks, light bulbs, and break room chairs is not worth the effort.
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